Arvind Sathi

This book explores cognitive behavior among Internet of Things. Using a series of current and futuristic examples – appliances, personal assistants, robots, driverless cars, customer care, engineering, monetization, and many more – the book covers use cases, technology and communication aspects of how machines will support individuals and organizations. This book examines the Cognitive Things covering a number of important questions: • What are Cognitive Things? • What applications can be driven from Cognitive Things – today and tomorrow? • How will these Cognitive Things collaborate with each and other, with individuals and with organizations? • What is the cognitive era? How is it different from the automation era? • How will the Cognitive Things support or accelerate human problem solving? • Which technical components make up cognitive behavior? • How does it redistribute the work-load between humans and machines? • What types of data can be collected from them and shared with external organizations? • How do they recognize and authenticate authorized users? How is the data safeguarded from potential theft? Who owns the data and how are the data ownership rights enforced? Overall, Sathi explores ways in which Cognitive Things bring value to individuals as well as organizations and how to integrate the use of the devices into changing organizational structures. Case studies are used throughout to illustrate how innovators are already benefiting from the initial explosion of devices and data. Business executives, operational managers, and IT professionals will understand the fundamental changes required to fully benefit from cognitive technologies and how to utilize them for their own success.

Collaboration to Optimize Action

Cognitive (Internet of) Things

IBM Redbooks

Wei-Dong Zhu

Manav Gupta

Ven Kumar

Sujatha Perepa

Craig Statchuk

Big data is currently one of the most critical emerging technologies. Organizations around the world are looking to exploit the explosive growth of data to unlock previously hidden insights in the hope of creating new revenue streams, gaining operational efficiencies, and obtaining greater understanding of customer needs. It is important to think of big data and analytics together. Big data is the term used to describe the recent explosion of different types of data from disparate sources. Analytics is about examining data to derive interesting and relevant trends and patterns, which can be used to inform decisions, optimize processes, and even drive new business models. With today's deluge of data comes the problems of processing that data, obtaining the correct skills to manage and analyze that data, and establishing rules to govern the data's use and distribution. The big data technology stack is ever growing and sometimes confusing, even more so when we add the complexities of setting up big data environments with large up-front investments. Cloud computing seems to be a perfect vehicle for hosting big data workloads. However, working on big data in the cloud brings its own challenge of reconciling two contradictory design principles. Cloud computing is based on the concepts of consolidation and resource pooling, but big data systems (such as Hadoop) are built on the shared nothing principle, where each node is independent and self-sufficient. A solution architecture that can allow these mutually exclusive principles to coexist is required to truly exploit the elasticity and ease-of-use of cloud computing for big data environments. This IBM® RedpaperTM publication is aimed at chief architects, line-of-business executives, and CIOs to provide an understanding of the cloud-related challenges they face and give prescriptive guidance for how to realize the benefits of big data solutions quickly and cost-effectively.

Building Big Data and Analytics Solutions in the Cloud

Arvind Sathi

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